Gain insight into the safety and efficacy of your drug
Model-based meta analysis (MBMA) uses highly curated clinical trial data (Certara’s Clinical Trial Outcomes Databases) and pharmacology models to increase drug development productivity, quantitatively inform portfolio management and improve clinical trial success.
Certara’s MBMA approach offers three key advantages.
- It supports bridging across studies, thereby enabling comparison of treatments and patient populations that may never have been tested together in the same clinical trial.
- Our MBMA models are based on pharmacologic principles which facilitate incorporating wider spectrum data with regard to dose, observation time, and clinical trial designs
- MBMA can be used to bring reality to synthetic patients and create synthetic control arms, offering advantages to typical synthetic control arms based on observational data.
Maximize your probability of success
Model-based meta analysis (MBMA) helps to answer important drug development questions including:
• Compare your drug vs the competition: What are the characteristics of the dose-response curves for existing drugs that are in the same class as a new compound?
• Optimize trial design: What is the impact of trial design features (e.g., time, endpoints) on treatment effects? How can we optimize dosing to maximize safety and efficacy?
• Inform go/no go, portfolio, marketing decisions: Can we differentiate the drug as best-in-class?
Competitive landscaping with MBMA
There are very few active comparator trials in drug development. However, it is often important to assess a compound’s safety and efficacy profile in comparison to the standard of care and/or competitor drugs in development.
Model-based meta analysis (MBMA) enables indirect comparison, taking into account the impact of treatment, patient population, and trial characteristics on responses to medications.
This type of analysis, also called ‘competitive landscaping,’ helps us to understand potential differentiators in the drug profile and determine whether a drug is superior in the same drug class or across drug classes.
CODEx and Clinical Trial Outcomes Databases
Certara’s model-based meta analysis (MBMA) projects use our Clinical Trial Outcomes Databases, which capture up-to-date information on trial design, patient characteristics, treatments, statistical analyses and safety and efficacy in more than 45 therapeutic areas.
We provide CODEX, an intuitive, interactive, web-based graphical interface, to unlock the value and richness of public and proprietary clinical outcome data. You can visualize, explore, analyze, and communicate the content of CODEX to a broad audience.
More realistic synthetic control arms with MBMA
MBMA-based synthetic control arms can more closely resemble the actual trial populations and trial conditions the treatments are evaluated in. Observational data can often be quite different from what is seen in a clinical trial.
Jaap’s research interests are applying modeling and simulation to optimize treatment strategies, trial designs, and drug development decision-making. He is the world’s leading expert on model-based meta analysis and has published extensively and received several awards for his academic contributions.
At Certara, Dr. Matthew Zierhut advances the integration of external aggregate clinical trial data into development decisions and commercial and regulatory strategy via model-based meta-analysis (MBMA). Matt works closely with clinical development teams to ensure MBMA is leveraged for optimal impact when making the most critical decisions.
Jaap’s research interests are applying modeling and simulation to optimize treatment strategies, trial designs, and drug development decision-making. He is the world’s leading expert on model-based meta analysis and has published extensively and received several awards for his academic contributions.
At Certara, Dr. Matthew Zierhut advances the integration of external aggregate clinical trial data into development decisions and commercial and regulatory strategy via model-based meta-analysis (MBMA). Matt works closely with clinical development teams to ensure MBMA is leveraged for optimal impact when making the most critical decisions.